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Generic advantages and disadvantages of wearables for monitoring of cardiac arrhythmia.

Generic advantages and disadvantages of wearables for monitoring of cardiac arrhythmia.

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The possibilities and implementation of wearable cardiac monitoring beyond atrial fibril-lation are increasing continuously. This review focuses on the real-world use and evolution of these devices for other arrhythmias, cardiovascular diseases and some of their risk factors beyond atrial fibrillation. The management of nonatrial fibrillation arrhy...

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... 1 summarizes different types of cardiac monitoring. Advantages and disadvantages of wearables for arrhythmia monitoring are shown in Figure 1. ...

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... Most photoplethysmography-based devices for HR monitoring use HRV algorithms to detect AF. Several international consensus documents and guidelines have accepted the newer technologies, based on HRV analysis, for the screening and detection of AF [15][16][17][18][19][20][21][22][23][24][25]. ...
... Such recordings have more, usually motion-related, technical artifacts. A massive amount of data collected by long-term recorders has become an analytical challenge [19]. ...
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Background: The ratio of the difference between neighboring RR intervals to the length of the preceding RR interval (x%) represents the relative change in the duration between two cardiac cycles. We investigated the diagnostic properties of the percentage of relative RR interval differences equal to or greater than x% (pRRx%) with x% in a range between 0.25% and 25% for the distinction of atrial fibrillation (AF) from sinus rhythm (SR). Methods: We used 1-min ECG segments with RR intervals with either AF (32,141 segments) or SR (32,769 segments) from the publicly available Physionet Long-Term Atrial Fibrillation Database (LTAFDB). The properties of pRRx% for different x% were analyzed using the statistical procedures and metrics commonly used to characterize diagnostic methods. Results: The distributions of pRRx% for AF and SR differ significantly over the whole studied range of x% from 0.25% to 25%, with particularly outstanding diagnostic properties for the x% range of 1.5% to 6%. However, pRR3.25% outperformed other pRRx%. Firstly, it had one of the highest and closest to perfect areas under the curve (0.971). For pRR3.25%, the optimal threshold for distinction AF from SR was set at 75.32%. Then, the accuracy was 95.44%, sensitivity was 97.16%, specificity was 93.76%, the positive predictive value was 93.85%, the negative predictive value was 97.11%, and the diagnostic odds ratio was 514. The excellent diagnostic properties of pRR3.25% were confirmed in the publicly available MIT-BIH Atrial Fibrillation Database. In a direct comparison, pRR3.25% outperformed the diagnostic properties of pRR31 (the percentage of successive RR intervals differing by at least 31 ms), i.e., so far, the best single parameter differentiating AF from SR. Conclusions: A family of pRRx% parameters has excellent diagnostic properties for AF detection in a range of x% between 1.5% and 6%. However, pRR3.25% outperforms other pRRx% parameters and pRR31 (until now, probably the most robust single heart rate variability parameter for AF diagnosis). The exquisite pRRx% diagnostic properties for AF and its simple computation make it well-suited for AF detection in modern ECG technologies (mobile/wearable devices, biopatches) in long-term monitoring. The diagnostic properties of pRRx% deserve further exploration in other databases with AF.
... Perhaps the next challenge is the development and implementation of artificial intelligence (AI) to improve the management and processing of diagnostic data collected in medical centers every day. Using AI could not only provide more efficient selection and sorting of incoming data, but also enable more detailed and broader association studies of more data, including novel biomarkers with unknown potential [23]. ...
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Due to distressing statistics concerning cardiovascular diseases, remote monitoring of cardiac implantable electronic devices (CIED) has received a priority recommendation in daily patient care. However, most bedside systems available so far are not optimal due to limited patient adherence. We report that smartphone app technology communicating with CIED improved the patient’s engagement and adherence, as well as the accuracy of atrial and ventricular arrhythmias diagnosis, thus offering more efficient treatment and, consequently, better patient clinical outcomes. Our findings are in concordance with previously published results for implantable loop recorders and pacemakers, and provide new insight for heart failure patients with an implanted cardiac resynchronization therapy defibrillator.
... Initial diagnosis of atrial fibrillation has been shown to reduce stroke incidence and public health costs, and the results indicate that this trend will continue. Duncker et al. (2021) subjected regarding atrial fibrillation, the real-world applications and developments of such technologies for various arrhythmias, cardiovascular illnesses, and risk indicators for these conditions. Holter monitors, event recorders, ECG patches, wristbands, and textiles are all part of a growing area of wearable technology in cardiology to treat non-atrial fibrillation arrhythmias. ...
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Irregular heartbeats are a primary indicator of Cardiovascular Disease (CVD), which is the leading cause of death in a developing smart city environment. Wearable devices can reliably monitor cardiac beats by producing Electrocardiogram (ECG) readings. The considerable value gained from a wireless wearable system allows for remote ECG assessment with continuous real-time functionality. The data collected from the wearable sensor network in the smart city platform gives timely alarms and treatment that could save lives. Cloud-based ECG methods can be accurate to a certain extent, as latency is still an existing problem. Cloud-based portals linked immediately to wearable devices can provide numerous advantages, such as reduced latency and a good level of service. Therefore, a novel cloud-based arrhythmia detection using the Recurrent Neural Network (RNN) (NC-RNN) method has been proposed for the ECG diagnosis with a wearable sensor in the smart city environment. The ECG signal collected from the wearable sensor involves three phase diagnosis stage. R-peak detection techniques are used for preliminary diagnostics in edge devices. The ECG signals are then classified using RNN at the edge device, with the severity of irregular beat detected in the ECG signal. Finally, a cloud platform classification method can evaluate the obtained ECG signals. While the proposed method's training session is runnable on the technically rich Cloud data centers, the interpretation unit is deployed over the cloud infrastructure for evaluating the ECG signals and setting off the emergency remedies with minimum latency. The simulation results of the suggested framework can accomplish effective ECG detection via wearable devices with high accuracy and less latency.
... The early identification and prediction of CVD is critical for improving healthcare outcomes, but this remains challenging. The development of a variety of sensors, communication networks, and wearable and portable devices has enabled the acquisition of multiple forms of data that have the potential to improve the diagnosis of CVD [7][8][9][10][11][12]. The manual analysis of often complex time-series data is difficult, requiring the development of algorithms that analyse such data to potentially enhance diagnosis. ...
... (3) Artificial intelligence, machine learning and deep learning Wearable devices are rapidly developing in the health fields for telemedicine, pa-tient monitoring, and mobile health (mHealth) systems. The role of these devices has been examined in the remote monitoring and diagnosis of common CVDs [10,11,102], and the opportunity and obstacles of these devices have been explored [103,104]. Specific barriers and knowledge gaps such as HR and activity tracking have been identified for the use of wearables in clinical cardiovascular healthcare [105]. ...
... Monitoring: wearable devices offer a way to continuously monitor health parameters such as heart rate and heart rhythm, etc., in a user-friendly, non-invasive way. The continuous monitoring of physiological parameters then offers a potential solution to more timely access to CVD-based healthcare [9][10][11][12][13][14]47,50,52,53,59,66]. ...
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Cardiovascular disease (CVD) is the world’s leading cause of mortality. There is significant interest in using Artificial Intelligence (AI) to analyse data from novel sensors such as wearables to provide an earlier and more accurate prediction and diagnosis of heart disease. Digital health technologies that fuse AI and sensing devices may help disease prevention and reduce the substantial morbidity and mortality caused by CVD worldwide. In this review, we identify and describe recent developments in the application of digital health for CVD, focusing on AI approaches for CVD detection, diagnosis, and prediction through AI models driven by data collected from wearables. We summarise the literature on the use of wearables and AI in cardiovascular disease diagnosis, followed by a detailed description of the dominant AI approaches applied for modelling and prediction using data acquired from sensors such as wearables. We discuss the AI algorithms and models and clinical applications and find that AI and machine-learning-based approaches are superior to traditional or conventional statistical methods for predicting cardiovascular events. However, further studies evaluating the applicability of such algorithms in the real world are needed. In addition, improvements in wearable device data accuracy and better management of their application are required. Lastly, we discuss the challenges that the introduction of such technologies into routine healthcare may face.
... The development of wearable technologies has opened up new avenues for the detection and management of cardiovascular illnesses and related risk factors. Heart rate and rhythm monitors and blood pressure monitors, for example, are now widely used outside of hospitals and can be purchased by individuals [64]. ...
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Background/Purpose: In 1891, Gerard Philips and his father Frederik created the Dutch multinational corporation Philips in Eindhoven. Its headquarters are in Amsterdam. Having divesting off its consumer electronics division, Philips is now focused completely on the health technology industry. The company has extensive experience in a wide range of healthcare-related fields, including cardiology, health technology, oncology, respiratory medicine, fertility and pregnancy. To make people's lives better through innovation, and to contribute to the creation of a world that is both more sustainable and healthier. Objective: This paper provides a case study of Philips' transformation from an electronics firm to a leading healthcare product producer. This paper also looks at the healthcare business as a whole, as well as the many technological advancement components of it. Design/Methodology/Approach: Secondary sources were used in this investigation, including journals and conference publications, annual reports, Philips Company websites, the internet, scholarly articles, and social media reviews. On the company, a SWOT analysis was performed. Findings/Results: The 131-year-old company’s growth as an electrical and electronic goods manufacturer has been meritorious. The company has ventured into the healthcare sector after 2010 and has a road ahead to become a pioneer in this sector. Conclusion: Philips Healthcare is a global player in the manufacture of healthcare equipment. The company has a robust R&D division which can aid in building more innovative healthcare products. By being more innovative the company can achieve its mission of improving global health and sustainability through technological advancements. Paper Type: Company analysis as a Research Case Study
... The development of wearable technologies has opened up new avenues for the detection and management of cardiovascular illnesses and related risk factors. Heart rate and rhythm monitors and blood pressure monitors, for example, are now widely used outside of hospitals and can be purchased by individuals [64]. ...
... AI can analyse phenomena occurring in ischemic myocardium [26]. For arrhythmias unrelated to atrial fibrillation, therapy is supported, but adequate accuracy and validation of the clinical pathways are required [27]. Artificial intelligence-based cIMT/PA segmentation methods have been developed to monitor the risk of CVD/stroke [28]. ...
... Directions for further research include the area of patient-specific preventive MI medicine consisting of: follow-up visits (e.g., once per month), daily use of accessories such as scales, smart bands, smartphones with appropriate software connected to a central database and clinical AI system [27], use of modifiable risk factors to improve the patient's health status and MI risk assessment, and inclusion of non-modifiable MI risk factors in AI-based analysis. ...
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The incidence of myocardial infarction (MI) is growing year on year around the world. It is considered increasingly necessary to detect the risks early, respond through preventive medicines and, only in the most severe cases, control the disease with more effective therapies. The aim of the project was to develop a relatively simple artificial-intelligence tool to assess the likelihood of a heart infarction for preventive medicine purposes. We used binary classification to determine from a wide variety of patient characteristics the likelihood of heart disease and, from a computational point of view, determine what the minimum set of characteristics permits. Factors with the highest positive influence were: cp, restecg and slope, whilst factors with the highest negative influence were sex, exang, oldpeak, ca, and thal. The novelty of the described system lies in the development of the AI for predictive analysis of cardiovascular function, and its future use in a specific patient is the beginning of a new phase in this field of research with a great opportunity to improve pre-clinical care and diagnosis, and accuracy of prediction in preventive medicine.
... Research on validation of ECG devices to evaluate exercise-related arrythmias in athletes is sparse despite their widespread and growing use. Recent systematic reviews assessed wearable devices to monitor cardiac health and ECG traces in the general population [21,22]. However, these findings cannot be directly applied to athletic populations as ECGs are assessed at baseline and not during high-intensity exercise where movement artifact and abnormal waveforms occur. ...
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Background: Athletes can experience exercise-induced transient arrythmias during high-intensity exercise or competition, which are difficult to capture on traditional Holter monitors or replicate in clinical exercise testing. The aim of this study was to investigate the reliability of a portable single channel ECG sensor and data recorder (PluxECG) and to evaluate the confidence and reliability in interpretation of ECGs recorded using the PluxECG during remote rowing. Methods: This was a two-phase study on rowing athletes. Phase I assessed the accuracy and precision of heart rate (HR) using the PluxECG system compared to a reference 12-lead ECG system. Phase II evaluated the confidence and reliability in interpretation of ECGs during ergometer (ERG) and on-water (OW) rowing at moderate and high intensities. ECGs were reviewed by two expert readers for HR, rhythm, artifact and confidence in interpretation. Results: Findings from Phase I found that 91.9% of samples were within the 95% confidence interval for the instantaneous value of the changing exercising HR. The mean correlation coefficient across participants and tests was 0.9886 (σ = 0.0002, SD = 0.017) and between the two systems at elevated HR was 0.9676 (σ = 0.002, SD = 0.05). Findings from Phase II found significant differences for the presence of artifacts and confidence in interpretation in ECGs between readers' for both intensities and testing conditions. Interpretation of ECGs for OW rowing had a lower level of reader agreement than ERG rowing for HR, rhythm, and artifact. Using consensus data between readers' significant differences were apparent between OW and ERG rowing at high-intensity rowing for HR (p = 0.05) and artifact (p = 0.01). ECGs were deemed of moderate-low quality based on confidence in interpretation and the presence of artifacts. Conclusions: The PluxECG device records accurate and reliable HR but not ECG data during exercise in rowers. The quality of ECG tracing derived from the PluxECG device is moderate-low, therefore the confidence in ECG interpretation using the PluxECG device when recorded on open water is inadequate at this time.
... There are multiple types of cardiac monitoring, as stated in [30], where different aspects are discussed: ...
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Over the last couple of years, in the context of the COVID-19 pandemic, many healthcare issues have been exacerbated, highlighting the paramount need to provide both reliable and affordable health services to remote locations by using the latest technologies such as video conferencing, data management, the secure transfer of patient information, and efficient data analysis tools such as machine learning algorithms. In the constant struggle to offer healthcare to everyone, many modern technologies find applicability in eHealth, mHealth, telehealth or telemedicine. Through this paper, we attempt to render an overview of what different technologies are used in certain healthcare applications, ranging from remote patient monitoring in the field of cardio-oncology to analyzing EEG signals through machine learning for the prediction of seizures, focusing on the role of artificial intelligence in eHealth.
... The use of this technology as a wearable has a few limitations, including motion artifacts, inaccuracies from differences in ambient light, high body mass index, skin moisture, hypovolemic states, and importantly darker skin tone [12,14,24,33,34]. Therefore, further developments focused on minimizing the effect of external factors would improve accuracy and encourage more widespread use [21]. ...
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Wearable devices stand to revolutionize the way healthcare is delivered. From consumer devices that provide general health information and screen for medical conditions to medical-grade devices that allow collection of larger datasets that include multiple modalities, wearables have a myriad of potential uses, especially in cardiovascular disorders. In this review, we summarize the underlying technologies employed in these devices and discuss the regulatory and economic aspects of such devices as well as the future implications of their use.